A ubiquitous method for street scale spatial data collection and analysis in challenging urban environments: mapping health risks using spatial video in Haiti

International Journal of Health Geographics

J Glenn Morris Jr¹  Jocelyn M Widmer³  Jason K Blackburn¹  Andrew Curtis² 

[1] Emerging Pathogens Institute, University of Florida, Gainesville, FL, 32610, USA;GIS, Health & Hazards Lab, Department of Geography, Kent State University, Kent, OH, 44242, USA;Urban Affairs and Planning, School of Public and International Affairs, Virginia Tech University, Blacksburg, VA, 24061, USA

关键词: Cholera;    Haiti;    Informal settlements;    Spatial video;   

Others  :  810176
DOI  :  10.1186/1476-072X-12-21

 received in 2013-03-07, accepted in 2013-03-30,  发布年份 2013

【 摘 要 】

Background

Fine-scale and longitudinal geospatial analysis of health risks in challenging urban areas is often limited by the lack of other spatial layers even if case data are available. Underlying population counts, residential context, and associated causative factors such as standing water or trash locations are often missing unless collected through logistically difficult, and often expensive, surveys. The lack of spatial context also hinders the interpretation of results and designing intervention strategies structured around analytical insights. This paper offers a ubiquitous spatial data collection approach using a spatial video that can be used to improve analysis and involve participatory collaborations. A case study will be used to illustrate this approach with three health risks mapped at the street scale for a coastal community in Haiti.

Methods

Spatial video was used to collect street and building scale information, including standing water, trash accumulation, presence of dogs, cohort specific population characteristics, and other cultural phenomena. These data were digitized into Google Earth and then coded and analyzed in a GIS using kernel density and spatial filtering approaches. The concentrations of these risks around area schools which are sometimes sources of diarrheal disease infection because of the high concentration of children and variable sanitary practices will show the utility of the method. In addition schools offer potential locations for cholera education interventions.

Results

Previously unavailable fine scale health risk data vary in concentration across the town, with some schools being proximate to greater concentrations of the mapped risks. The spatial video is also used to validate coded data and location specific risks within these “hotspots”.

Conclusions

Spatial video is a tool that can be used in any environment to improve local area health analysis and intervention. The process is rapid and can be repeated in study sites through time to track spatio-temporal dynamics of the communities. Its simplicity should also be used to encourage local participatory collaborations.

【 授权许可】

   
2013 Curtis et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Ali M, Sur D, Lopez AL, Kanungo S, Ochiai RL, Manna B, Kim DR, Deen J, Bhattacharya SK, Clemens JD: Community participation in two vaccination trials in slums of Kolkata, India: a multi-level analysis. J Health Popul Nutr 2010, 28:450-457.
• [2]Hussain A, Ali SM, Kvale G: Determinants of mortality among children in the urban slums of Dhaka city, Bangladesh. Trop Med Int Health 1999, 4:758-764.
• [3]Kanungo S, Sur D, Ali M, You YA, Pal D, Manna B, Niyogi SK, Sarkar B, Bhattacharya SK, Clemens JD: Clinical, epidemiological, and spatial characteristics of Vibrio parahaemolyticus diarrhea and cholera in the urban slums of Kolkata, India. BMC Publ Health 2012, 12:830. BioMed Central Full Text
• [4]Riley LW, Ko AI, Unger A, Reis MG: Slum health: diseases of neglected populations. BMC Int Health Hum Rights 2007, 7:2. BioMed Central Full Text
• [5]Sur D, Deen JL, Manna B, Niyogi SK, Deb AK, Kanungo S, Sarkar BL, Kim DR, Danovaro-Holliday MC, Holliday K: The burden of cholera in the slums of Kolkata, India: data from a prospective, community based study. Arch Dis Child 2005, 90:1175-1181.
• [6]Sur D, von Seidlein L, Manna B, Dutta S, Deb AK, Sarkar BL, Kanungo S, Deen JL, Ali M, Kim DR: The malaria and typhoid fever burden in the slums of Kolkata, India: data from a prospective community-based study. Trans R Soc Trop Med Hyg 2006, 100:725-733.
• [7]Unger A, Riley LW: Slum health: from understanding to action. PLoS Med 2007, 4:1561-1566.
• [8]Ali M, Rasool S, Park J-K, Saeed S, Ochiai RL, Nizami Q, Acosta CJ, Bhutta Z: Use of satellite imagery in constructing a household GIS database for health studies in Karachi, Pakistan. Int J Health Geogr 2004, 3:20. BioMed Central Full Text
• [9]Dewan AM, Yamaguchi Y: Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960–2005. Environ Monit Assess 2009, 150:237-249.
• [10]Gerke M, Kerle N: Automatic structural seismic damage assessment with airborn e oblique pictometry imagery. Photogramm Eng Remote Sens 2011, 77:885-898.
• [11]Turkstra J: Visible and non-visible slum, Libya and Somalia. Expert Group Meeting on Slum Mapping at ITC in Enschede Netherlands 2008, 21-23.
• [12]Gunter AW: Getting it for free: Using Google earth™ and IL WIS to map squatter settlements in Johannesburg. Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009 2009, III-388-III-391.
• [13]Wampler PJ, Rediske RR, Molla AR: Using ArcMap, Google Earth, and Global Positioning Systems to select and locate random households in rural Haiti. Int J Health Geogr 2013, 12:3. BioMed Central Full Text
• [14]Kun Y, Le-Ping S, Yi-Xin H, Guo-Jing Y, Feng W, De-Rong H, Wei L, Jian-Feng Z, Yong-Sheng L, Xiao-Nong Z: A real-time platform for monitoring schistosomiasis transmission supported by Google Earth and a web-based geographical information system. Geospat Health 2012, 6:195-203.
• [15]Chang AY, Parrales ME, Jimenez J, Sobieszczyk ME, Hammer SM, Copenhaver DJ, Kulkarni RP: Combining Google Earth and GIS mapping technologies in a dengue surveillance system for developing countries. Int J Health Geogr 2009, 8:49. BioMed Central Full Text
• [16]Liu SB, Palen L: The new cartographers: crisis map mashups and the emergence of neogeographic practice. Cartogr Geogr Inf Sci 2010, 37:69-90.
• [17]Zook M, Graham M, Shelton T, Gorman S: Volunteered geographic information and crowdsourcing disaster relief: a case study of the Haitian earthquake. World Medical & Health Policy 2012, 2:7-33.
• [18]Haklay M: How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets. Environment and planning. B, Planning & design 2010, 37:682.
• [19]Haklay MM, Basiouka S, Antoniou V, Ather A: How many volunteers does it take to map an area well? The validity of Linus’ law to volunteered geographic information. Cartogr J 2010, 47:315-322.
• [20]Reis RB, Ribeiro GS, Felzemburgh RD, Santana FS, Mohr S, Melendez AX, Queiroz A, Santos AC, Ravines RR, Tassinari WS: Impact of environment and social gradient on Leptospira infection in urban slums. PLoS Negl Trop Dis 2008, 2:e228.
• [21]Angeles G, Lance P, Barden-O'Fallon J, Islam N, Mahbub A, Nazem NI: International Journal of Health Geographics. Int J Health Geogr 2009, 8:32. BioMed Central Full Text
• [22]Hoyt L, Khosla R, Canepa C: Leaves, pebbles, and chalk: building a public participation GIS in New Delhi, India. Journal of Urban Technology 2005, 12:1-19.
• [23]Paar P, Rekittke J: Low-cost mapping and publishing methods for landscape architectural analysis and design in slum-upgrading projects. Future Internet 2011, 3:228-247.
• [24] : Distance to hospital and utilization of surgical services in Haiti: do children, delivering mothers, and patients with emergent surgical conditions experience greater geographical barriers to surgical care? Int J Health Plann Manage 2012,  : .
• [25]Braun B, Aßheuer T: Floods in megacity environments: vulnerability and coping strategies of slum dwellers in Dhaka/Bangladesh. Nat Hazard 2011, 58:771-787.
• [26]Curtis A, Duval-Diop D, Novak J: Identifying spatial patterns of recovery and abandonment in the post-Katrina Holy Cross neighborhood of New Orleans. Cartogr Geogr Inf Sci 2010, 37:45-56.
• [27]Curtis AJ, Mills JW, McCarthy T, Fotheringham AS, Fagan WF: Space and time changes in neighborhood recovery after a disaster using a spatial video acquisition system. In Geospatial Techniques in Urban Hazard and Disaster Analysis. Edited by Showalter PS, Lu Y. Springer Netherlands; 2010:373-392.
• [28]Dewan AM, Corner R, Hashizume M, Ongee ET: Typhoid Fever and Its Association with Environmental Factors in the Dhaka Metropolitan Area of Bangladesh: A Spatial and Time-Series Approach. PLoS Negl Trop Dis 2013, 7:e1998.
• [29]Reis RB, Ribeiro GS, Felzemburgh RD, Santana FS, Mohr S, Melendez AX, Queiroz A, Santos AC, Ravines RR, Tassinari WS: Impact of environment and social gradient on Leptospira infection in urban slums. PLoS Negl Trop Dis 2008, 2:e228.
• [30]Blackburn J, Curtis A, Mujica F, Jones F, Dorn P, Coates R: The Development of the Chagas’ Online Data Entry System (CODES GIS). Trans GIS 2008, 12:249-265.
• [31]Steiniger S, Bocher E: An overview on current free and open source desktop GIS developments. Int J Geogr Inf Sci 2009, 23:1345-1370.
• [32]Fotheringham AS, Brunsdon C, Charlton M: Geographically weighted regression: the analysis of spatially varying relationships. Chichester: Wiley; 2002.
• [33]Corbane C, Carrion D, Lemoine G, Broglia M: Comparison of Damage Assessment Maps Derived from Very High Spatial Resolution Satellite and Aerial Imagery Produced for the Haiti 2010 Earthquake. Earthquake Spectra 2011, 27:199-218.
• [34]Piarroux R, Barrais R, Faucher B, Haus R, Piarroux M, Gaudart J, Magloire R, Raoult D: Understanding the cholera epidemic, Haiti. Emerg Infect Dis 2011, 17:1161-1168.
• [35]Rinaldo A, Bertuzzo E, Mari L, Righetto L, Blokesch M, Gatto M, Casagrandi R, Murray M, Vesenbeckh SM, Rodriguez-Iturbe I: Reassessment of the 2010–2011 Haiti cholera outbreak and rainfall-driven multiseason projections. Proc Natl Acad Sci 2012, 109:6602-6607.
• [36]Schuller M: Haiti’s disaster after the disaster: The IDP camps and cholera. The Journal of Humanitarian Assistance, Available: http://jha. ac/2010/12/13/haiti% E2 2010., 80
• [37]Bertuzzo E, Mari L, Righetto L, Gatto M, Casagrandi R, Blokesch M, Rodriguez-Iturbe I, Rinaldo A: Prediction of the spatial evolution and effects of control measures for the unfolding Haiti cholera outbreak. Geophys Res Lett 2011., 38L06403
• [38]Walton DA, Ivers LC: Responding to cholera in post-earthquake Haiti. N Engl J Med 2011, 364:3-5.
• [39]Bengtsson L, Lu X, Thorson A, Garfield R, von Schreeb J: Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti. PLoS Med 2011, 8:e1001083.
• [40]Lu X, Bengtsson L, Holme P: Predictability of population displacement after the 2010 Haiti earthquake. Proc Natl Acad Sci 2012, 109:11576-11581.
• [41]Smego RA Jr, Gebrian B, Desmangels G: Cutaneous manifestations of anthrax in rural Haiti. Clin Infect Dis 1998, 26:97-102.
• [42]Peck RN, Fitzgerald DW: Cutaneous anthrax in the Artibonite Valley of Haiti: 1992–2002. AmJTrop Med Hyg 2007, 77:806-811.
• [43]Kaufmann AF, Dannenberg AL: Age as a Risk Factor for Cutaneous Human Anthrax: Evidence from Haiti, 1973-1974. Emerg Infect Dis 2002, 8:874-875.
• [44]Curtis A, Mills JW, Kennedy SW, Kulkarni A, Tauer TM: A methodology for assessing dynamic fine scale built environments and crime: A case study of the Lower 9th Ward after Hurricane Katrina in M. Springer Netherlands: Leitner Crime Modeling and Mapping Using Geospatial Technologies; 2013:203-226.
• [45]Curtis A, Mills JW: Crime in urban post-disaster environments: A methodological framework from New Orleans. Urban Geography 2011, 32:488-510.
• [46]Khadr Z, Nour el Dein M, Hamed R: Using GIS in constructing area-based physical deprivation index in Cairo Governorate, Egypt. Habitat International 2010, 34:264-272.
• [47]Rushton G, Lolonis P: Exploratory spatial analysis of birth defect rates in an urban population. Stat Med 1998, 15:717-726.
• [48]Rushton G, Peleg I, Banerjee A, Smith G, West M: Analyzing geographic patterns of disease incidence: rates of late-stage colorectal cancer in Iowa. J Med Syst 2004, 28:223-236.
• [49]Lentz JA, Blackburn JK, Curtis AJ: Evaluating patterns of a white-band disease (WBD) outbreak in Acropora palmata using spatial analysis: a comparison of transect and colony clustering. PLoS One 2011, 6:e21830.